Paper: https://arxiv.org/abs/2204.00598
https://socraticmodels.github.io/
Twitter: https://twitter.com/andyzengtweets/status/1512089759497269251
Abstract: " Large foundation models can exhibit unique capabilities depending on the domain of data they are trained on. While these domains are generic, they may only barely overlap. For example, visual-language models (VLMs) are trained on Internet-scale image captions, but large language models (LMs) are further trained on Internet-scale text with no images (e.g. from spreadsheets, to SAT questions). As a result, these models store different forms of commonsense knowledge across different domains. In this work, we show that this model diversity is symbiotic, and can be leveraged to build AI systems with structured Socratic dialogue -- in whi…
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New version of paper is linked to in the DALL-E 2 blog post and also here (pdf file format).
Tweet announcing updated paper.
Older version of paper (pdf file format).
Original Reddit post.
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A team of scientists have created a new AI-based tool to help lock up greenhouse gases like CO2 in porous rock formations faster and more precisely than ever before. Carbon capture technology, also referred to as carbon sequestration, is a climate change mitigation method that redirects CO2 emitted from power plants back underground. While doing Read article >
The post Rock On: Scientists Use AI to Improve Sequestering Carbon Underground appeared first on NVIDIA Blog.
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In many industries, it’s critical to extract custom entities from documents in a timely manner. This can be challenging. Insurance claims, for example, often contain dozens of important attributes (such as dates, names, locations, and reports) sprinkled across lengthy and dense documents. Manually scanning and extracting such information can be error-prone and time-consuming. Rule-based software […]
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Here's the blogpost estimating the cost.
What would it cost you to train PaLM using cloud computing (and you're not Google)? Something around $9M to $17M.
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I am thinking of building a graph classifier that takes in graphs and labels the incoming graph.
The dataset of interest to me is RadGraph: https://arxiv.org/abs/2106.14463
The issue I am having is that the graphs in RadGraph are disconnected in nature (on average 20 disconnected components), making it difficult for the various graph encoders I am aware of to do a good job classifying the graphs.
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Implementation here
Looks like they manually calculate the gradient? I'm very curious how much of a difference this makes!
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https://outsystems-ai-reading-group.github.io/
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Hi Everyone!
We have released the preprint and google colab demo for our paper FaceSigns. FaceSigns embeds a secret bit-string as a semi-fragile watermark in the image pixels. The message is recoverable if benign image operations such as color/contrast adjustment, JPEG compression, Instagram filters are applied. However, the message cannot be decoded if the image is facially tampered (eg. DeepFake manipulation) . This selective fragility allows reliable detection of DeepFake manipulations applied on images signed using FaceSigns.
Try out our google colab demo to see message encoding and decoding using FaceSigns!
Paper: https://arxiv.org/abs/2204.01960
Project Webpage: https://shehzeen.github.io/facesigns
Demo: https://github.com/paarthneekhara/FaceSignsDemo
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GeForce NOW is about bringing new experiences to gamers. This GFN Thursday introduces game demos to GeForce NOW. Members can now try out some of the hit games streaming on the service before purchasing the full PC version — including some finalists from the 2021 Epic MegaJam. Plus, look for six games ready to stream Read article >
The post Try This Out: GFN Thursday Delivers Instant-Play Game Demos on GeForce NOW appeared first on NVIDIA Blog.
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Out of curiosity, how long would it take to implement a paper like this one? https://arxiv.org/abs/2104.07750
It has PPO agents in MARL, all of them with multihead attention performed on the observation, in such a way that an attention map is created for each agent. This attention map has information about how strongly each agent is attending to various elements of the environment. With KL divergence, the agents are rewarded for minimizing the difference between their attention maps.
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Hey there, just a heads up we at The Gradient just published a new article discussing explainability -
"This article uses the common backdrop of competitive games to explore the ways in which domain experts adapt to new technologies that lack explainability. I illustrate how interpretations vary based on user experience and model architecture, and how special care must be taken when adapting models to human-centric problems."
Check it out here if you think it's interesting / worth discussing:
Reading the Tea Leaves: Expert End-Users Explaining the Unexplainable
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Meet the electric vehicle that’s quick-witted and fully outfitted. Last week, NIO began deliveries of its highly anticipated ET7 fully electric vehicle, in Hefei, China. The full-size luxury sedan is the first production vehicle built on the NIO Adam supercomputer, powered by four NVIDIA DRIVE Orin systems-on-a-chip (SoCs). The production launch of its flagship sedan Read article >
The post Fast and Luxurious: The Intelligent NIO ET7 EV Built on NVIDIA DRIVE Orin Arrives appeared first on NVIDIA Blog.
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In its debut in the industry MLPerf benchmarks, NVIDIA Orin, a low-power system-on-chip based on the NVIDIA Ampere architecture, set new records in AI inference, raising the bar in per-accelerator performance at the edge. Overall, NVIDIA with its partners continued to show the highest performance and broadest ecosystem for running all machine-learning workloads and scenarios Read article >
The post NVIDIA Orin Leaps Ahead in Edge AI, Boosting Leadership in MLPerf Tests appeared first on NVIDIA Blog.
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Amazon Rekognition Custom Labels is a fully managed computer vision service that allows developers to build custom models to classify and identify objects in images that are specific and unique to your business. Rekognition Custom Labels doesn’t require you to have any prior computer vision expertise. You can get started by simply uploading tens of […]
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A new technique compares the reasoning of a machine-learning model to that of a human, so the user can see patterns in the model’s behavior.
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Perceiver and PerceiverIO (https://arxiv.org/abs/2107.14795) appear to offer significantly improved FLOP efficiency, but new LLMs (including Deepmind's own Gopher) don't use it.
What gives? Is it still too new, or is the Perceiver architecture not appropriate for LLMs?
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Saw this posted on Schmidhuber's Twitter:
Meta-Learning Machines in a Single Lifelong Trial: lecture video (24 min) presented at meta-learning workshops at ICML 2020 and NeurIPS 2021. URL of talk: https://youtu.be/2GgGVdkq2bU
Abstract
The most widely used machine learning algorithms were designed by humans and thus are hindered by our cognitive biases and limitations. Can we also construct meta-learning algorithms that can learn better learning algorithms so that our self-improving AIs have no limits other than those inherited from computability and physics? This question has been a main driver of my research since I wrote a thesis on it in 1987. In the past decade, it has become a driver of many other people's research as well. Here I summarize our work starting in 1994 on meta-reinforcement learning with self-modifying policies in a single lifelong trial, and - since 2003 - mathematically optimal meta-learning through the self-referential Gödel Machine. This talk was previously presented at meta-learning workshops at ICML 2020 and NeurIPS 2021. Many additional publications on meta-learning can be found at https://people.idsia.ch/~juergen/metalearning.html
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Autoregressive models like GPT-2 do fairly well in text generation. Is it possible to do the same for graph data? A transformer based model Graphormer has recently shown its effectiveness in graph representation learning. Is there any way I can train Graphormer or any other model to generate graphs from an initial graph context?
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MIT researchers design a robot that has a trick or two up its sleeve.
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The built-in Amazon SageMaker XGBoost algorithm provides a managed container to run the popular XGBoost machine learning (ML) framework, with added convenience of supporting advanced training or inference features like distributed training, dataset sharding for large-scale datasets, A/B model testing, or multi-model inference endpoints. You can also extend this powerful algorithm to accommodate different requirements. […]
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Research over the past few years has shown that machine learning (ML) models are vulnerable to adversarial inputs, where an adversary can craft inputs to strategically alter the model’s output (in image classification, speech recognition, or fraud detection). For example, imagine you have deployed a model that identifies your employees based on images of their […]
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Square/Enix presents the fictional city of Midgar in Final Fantasy VII Remake at a filmic level of detail. Epic’s Fortnite bathes its environments in ray-traced sunlight, simulating how light bounces in the real world. And artists at Lucasfilm revolutionized virtual production techniques in The Mandalorian, using synchronized NVIDIA RTX GPUs to drive pixels on LED Read article >
The post Unreal Engine and NVIDIA: From One Generation to the Next appeared first on NVIDIA Blog.
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A dozen companies today received NVIDIA’s highest award for partners, recognizing their impact on AI education and adoption across such industries as education, federal, healthcare and technology. The winners of the 2021 NPN Americas Partner of the Year Awards have created a profound impact on AI by helping customers meet the demands of recommender systems, Read article >
The post Green Teams Achieve the Dream: NVIDIA Announces NPN Americas Partners of the Year appeared first on NVIDIA Blog.
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Given a NN class, is there something specific we need to care of when converting *args and **kwargs to a canonical kwarg representation? I ask this because in this code from Google (https://github.com/google-research/google-research/blob/c56b47713b08c95ad427d5f93ee0dbb9ad008964/social_rl/multiagent_tfagents/joint_attention/attention_networks.py#L557) they use a TFDecorator-aware replacement for inspect.getcallargs, instead of using getcallargs directly. So my questions are:
- Is it possible to use inspect.getcallargs to convert *args and **kwargs to a canonical kwarg representation?
- If no, is there an equivalent in PyTorch? I couldn't find any, so I was wondering how people go about that.
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📑 The Random R-GCN code has just been released!
📝 With just a few lines of code, you can now create embeddings of entities in a Knowledge Graph.
Minimal example on how to create embeddings with RR-GCN
💡 RR-GCN does not require training and is competitive to fully trained R-GCNs.
👉 https://github.com/predict-idlab/RR-GCN
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As more organizations move to machine learning (ML) to drive deeper insights, two key stumbling blocks they run into are labeling and lifecycle management. Labeling is the identification of data and adding labels to provide context so an ML model can learn from it. Labels might indicate a phrase in an audio file, a car […]
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Amazon Kendra is an intelligent search service powered by machine learning (ML). Amazon Kendra reimagines search for your websites and applications so your employees and customers can easily find the content they’re looking for, even when it’s scattered across multiple locations and content repositories within your organization. Amazon Kendra supports a variety of document formats, […]
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Primary focus will be to advance and promote technology, innovation, and entrepreneurship across the school.
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Posted by Sharan Narang and Aakanksha Chowdhery, Software Engineers, Google Research
In recent years, large neural networks trained for language understanding and generation have achieved impressive results across a wide range of tasks. GPT-3 first showed that large language models (LLMs) can be used for few-shot learning and can achieve impressive results without large-scale task-specific data collection or model parameter updating. More recent LLMs, such as GLaM, LaMDA, Gopher, and Megatron-Turing NLG, achieved state-of-the-art few-shot results on many tasks by scaling model size, using sparsely activated modules, and training on larger datasets from more diverse sources. Yet much work remains in understanding the capabilities that emerge with few-shot learning as we push the limits of …
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Pekka Varis’s artistry has come a long way from his early days as a self-styled “punk activist” who spray painted during the “old school days of hip hop in Finland.”
The post Meet the Omnivore: Videographer Makes Digital Walls, Virtual Homes Pop With NVIDIA Omniverse appeared first on NVIDIA Blog.
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https://www.lesswrong.com/posts/midXmMb2Xg37F2Kgn/new-scaling-laws-for-large-language-models
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For the MIT Schwarzman College of Computing dean, bringing disciplines together is the best way to address challenges and opportunities posed by rapid advancements in computing.
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I am looking at this code from Google (https://github.com/google-research/google-research/blob/master/social_rl/multiagent_tfagents/joint_attention/attention_networks.py).
At line 639, the LSTM is called. The first two inputs are the state and the network state, but I don't understand what the latter is.
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Dear community, I hope I do not violate rules by advertizing our Call for Papers here. In a nutshell, submissions can be robustness or OOD datasets and new metrics which we will consolidate in one benchmark. More infos on our website.
I am happy to answer any questions in regards to the call.
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Hello! I am doing an independent (non-academic) research study about AI text generation as relates to poetry and reader interpretation. The results of the study will be presented in a YouTube video.
I would really appreciate if some folks could take approximately 20-25 minutes to take this anonymous survey I put together. It involves reading some poems and answering questions about those poems. Thank you so much for the help!
https://form.jotform.com/220880249866062
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AlphaGo - The Movie on Youtube
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Excited to share our latest research. The cyclemoid activation was inspired by the success of cyclical learning rate. Moreover, it has nice mathematical properties to stabilize gradients and maintain strong gradient signals in desired regions during training.
We designed it as a drop-in replacement for ReLU, and we would love to hear what you think.
The code is up on GitHub, and the preprint should be up soon, too: https://github.com/rasbt/cyclemoid-pytorch
PS: Currently, we only have a PyTorch implementation but would welcome it if someone could port it to TensorFlow/Keras (my Tf/Keras skills are just too rusty for it.)
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Hello
I am working on an extension of this implementation https://github.com/philtabor/Youtube-Code-Repository/tree/master/ReinforcementLearning/ICM of the intrinsic curiosity module. It uses A3C(Actor -critic) as a policy and the ICM is a bolt on module.
I need to fix the seeds for reproducibility but no matter what i have tried I cannot achieve it.
The implementation uses multithreading on cpu and plays on the oepnai gym cartpole or atari environments.
I believe that it has something to do with multithreading but im not sure.
Could someone know what is the solution?
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I've tried various methods of using AI to generate April Fools pranks for you to play on other people (although often they turned out to be pranks you play on yourself). But this is the first time I've tried to generate pranks for a computer to
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AI Weirdness: the strange side of machine learning
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LAION-5B: A new era of open large-scale multi-modal datasets.
Twitter thread.
Related: [P] LAION-400M: open-source dataset of 400 million image-text pairs.
I am not affiliated with this project.
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I really would like to do some visualization of my ideas. I found the animation in the google ai blog:
https://ai.googleblog.com/2022/02/4d-net-learning-multi-modal-alignment.html
Anyone knows how to do this stuff, especially with the flowing lines? Any software suggestions?
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Data science and data engineering teams spend a significant portion of their time in the data preparation phase of a machine learning (ML) lifecycle performing data selection, cleaning, and transformation steps. It’s a necessary and important step of any ML workflow in order to generate meaningful insights and predictions, because bad or low-quality data greatly […]
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I asked a while back how to save state of an episode when this was my original intention.
even a quick perusal of an environment's code reveals interesting information that's helpful to using gym as a whole
https://www.github.com/openai/gym/tree/master/gym/envs
I think it'd be interesting if papers supplied seed numbers for their test andntraining runs, where they're pulled from an array of ints contained in the agent.
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At Monterrey Tec, MIT’s president discusses the impact of education in addressing global issues.
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A new technique could enable a robot to manipulate squishy objects like pizza dough or soft materials like clothing.
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In addition to GFN Thursday, it’s National Tater Day. Hooray! To honor the spud-tacular holiday, we’re closing out March with seven new games streaming this week. And a loaded 20+ titles are coming to the GeForce NOW library in April to play — even on a potato PC, thanks to GeForce NOW. Plus, the GeForce Read article >
The post An A-peel-ing GFN Thursday Sprouts 20+ New Games Coming to GeForce NOW in April appeared first on NVIDIA Blog.
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Compared to other programming exercises, a machine learning project is a blend of code and data. You need both to […]
The post A Guide to Getting Datasets for Machine Learning in Python appeared first on Machine Learning Mastery.
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More than a year ago, I wrote an article regarding some key obstacles that someone may face regarding working with AI in the medical field. A few days ago I submitted that article to "Towards Data Science", a 'Medium' based online publication. It got published yesterday. I am giving the link here. If anyone is interested in that topic, you can take a look. It mainly focuses on the part that - even if you have some previous experience in working with machine learning, there are some things you must know and be aware of before working with medical datasets. Link - https://towardsdatascience.com/some-key-challenges-in-building-an-ai-model-for-medical-diagnosis-63f7438f14a
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https://twitter.com/erik_nijkamp/status/1508956485379715072
Paper: https://arxiv.org/abs/2203.13474
Blog: https://blog.salesforceairesearch.com/codegen/
Code: https://github.com/salesforce/CodeGen
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Say given an MLP of 2 layers with non-linearity, are there established papers which explore if the sets of weights obtained after 2 trials of training end up with 'similar' weights.
From an old stackexchange thread(2017) two possible methods outlined are. 1. Compare similarity on the predictions on validation inputs. 2. Instead of comparing pairwise similarity, simply concat them and use t-sne for dimensionality reduction. Based on a 2009 work.
Link: https://cs.stackexchange.com/questions/74488/measuring-difference-between-two-sets-of-neural-network-weights
Does anyone know of any recent work which tackles this problem ?
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I notice that there is almost no papers in this area since 2020. And the rank of WSOD hasn't been updated since 2020:https://paperswithcode.com/sota/weakly-supervised-object-detection-on-pascal-1
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It provides:
A repository of modular and composable building blocks (models, fusion layers, loss functions, datasets and utilities).
A repository of examples that show how to combine these building blocks with components and common infrastructure from across the PyTorch Ecosystem to replicate state-of-the-art models published in the literature. These examples should serve as baselines for ongoing research in the field, as well as a starting point for future work.
https://github.com/facebookresearch/multimodal
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https://imgur.com/UOdDUFH
From the linked image I am wondering what tau is (the tau looks like a small r in the image unless you zoom in)? Is it a hard coded value like kappa (k)? If not how is the value for tau determined when Dyna Q+ runs?
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Four words: smart, sustainable, Super Bowl. Polestar’s commercial during the big game made it clear no-compromise electric vehicles are now mainstream. Polestar Chief Operating Officer Dennis Nobelius sees driving enjoyment and autonomous-driving capabilities complementing one another in sustainable vehicles that keep driving — and the driver — front and center. NVIDIA’s Katie Washabaugh spoke with Read article >
The post Polestar’s Dennis Nobelius on the Sustainable Performance Brand’s Plans appeared first on NVIDIA Blog.
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By Rex Ahlstrom, CTO & EVP Growth & Innovation, Syniti The modern enterprise is composed of a variety of systems, each of which holds data the company needs to conduct business: information about products, services, suppliers, customers, and more. This is the master data, and master data collected by these disparate systems is often stored… Read More »The Increasing Importance of Master Data Management for Your Business: A Primer
The post The Increasing Importance of Master Data Management for Your Business: A Primer appeared first on Data Science Central.
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Paper: https://arxiv.org/abs/2203.13448
Code: https://github.com/lijuncheng16/AudioTaggingDoneRight
For anyone who's interested in AudioSet (2million youtube videos' sound). This is the SOTA comparison of models and training procedures.
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Jonathan came on the Weaviate Podcast to discuss the story of MosaicML, their new open-source Python library for Efficient Deep Learning called Composer, Pareto Curves of Training Time X Accuracy, Model Surgey augmentations, Maximizing CPU and GPU throughput, and many more! I hope you find this useful, happy to continue discussions of what Jonathan presented!
https://www.youtube.com/watch?v=ZiBkspwrICA
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Google researchers created JAX to conduct NumPy computations on GPUs and TPUs. DeepMind uses it to help and expedite its research, and it is increasingly gaining popularity. Differentiation with grad(), vectorization with map(), and JIT-compilation (just-in-time) with jit are some of the composable functions required for machine learning research in JAX (). As a result, adding a JAX-based workload to the Flower code samples is a must-have. The combination of JAX and Flower allows ML and FL researchers to employ the deep learning framework that their projects demand. The updated code example now serves as a template for migrating existing JAX projects to a federated environment.
It’s pretty simple to put up a centralized machine learning architecture, and the JAX developer documentation has multiple examples. Because the ML model parameters are stored in the DeviceArray data format, setting up the federated workload requires some knowledge of JAX. To be compatible with the Flower NumPyClient, those arguments must be converted to NumPy ndarrays. The JAX meets Flower example below demonstrates how a Flower setup might work.
Continue Reading
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Sponsored Post Building successful machine learning products requires mastering ML Strategy, including problem formulation, evaluation, and tactics for dealing with […]
The post Interactive ML Strategy course with Foster Provost starting April 7 appeared first on Machine Learning Mastery.
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Datasets from real-world scenarios are important for building and testing machine learning models. You may just want to have some […]
The post A Guide to Obtaining Time Series Datasets in Python appeared first on Machine Learning Mastery.
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Today, customers interact with brands over an increasingly large digital and offline footprint, generating a wealth of interaction data known as behavioral data. As a result, marketers and customer experience teams must work with multiple overlapping tools to engage and target those customers across touchpoints. This increases complexity, creates multiple views of each customer, and […]
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This is blog post is co-written by Theresa Cabrera Menard, an Applied Scientist/Geographic Information Systems Specialist at The Nature Conservancy (TNC) in Hawaii. In recent years, Amazon and AWS have developed a series of sustainability initiatives with the overall goal of helping preserve the natural environment. As part of these efforts, AWS Professional Services establishes […]
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Amazon SageMaker Autopilot helps you complete an end-to-end machine learning (ML) workflow by automating the steps of feature engineering, training, tuning, and deploying an ML model for inference. You provide SageMaker Autopilot with a tabular data set and a target attribute to predict. Then, SageMaker Autopilot automatically explores your data, trains, tunes, ranks and finds […]
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“I am a visionary,” says an AI, kicking off the latest installment of NVIDIA’s I AM AI video series. Launched in 2017, I AM AI has become the iconic opening for GTC keynote addresses by NVIDIA founder and CEO Jensen Huang. Each video, with its AI-created narration and soundtrack, documents the newest advances in artificial Read article >
The post Latest ‘I AM AI’ Video Features Four-Legged Robots, Smart Cell Analysis, Tumor-Tracking Tech and More appeared first on NVIDIA Blog.
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When Tanish Tyagi published his first research paper a year ago on deep learning to detect dementia, it started a family-driven pursuit. Great-grandparents in his family had suffered from Parkinson’s, a genetic disease that affects more than 10 million people worldwide. So the now 16-year-old turned to that next, together with his sister, Riya, 14. Read article >
The post Teens Develop Handwriting-Recognition AI for Detecting Parkinson’s Disease appeared first on NVIDIA Blog.
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Associate professor and principal investigator with the MIT Schwarzman College of Computing’s Science Hub discusses the future of robotics and the importance of industry-academia collaborations.
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MIT AI Hardware Program launches with five inaugural companies to advance AI technologies for the next decade.
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Inventory management is an essential part of any eCommerce business. Especially if you are an eCommerce business owner juggling multiple sales channels, it can save you a lot of effort. However, manually managing your inventories is also a recipe for error. Also, let’s not forget the time you have to spend and the painful process… Read More »Automated Inventory Management System: An Ultimate Guide for 2022 and Beyond
The post Automated Inventory Management System: An Ultimate Guide for 2022 and Beyond appeared first on Data Science Central.
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Statistics gives business owners the freedom to evaluate how their websites are performing. The evaluation involves a couple of things: the bounce rate and the exit rate. But what is the difference between bounce rate and exit rate? This is a point of discussion that requires you to have an open mind to grasp the… Read More »What is the Difference Between Bounce Rate and Exit Rate?
The post What is the Difference Between Bounce Rate and Exit Rate? appeared first on Data Science Central.
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There is no denying the importance of the internet and IT in the business scene. Businesses hailing from all sectors are dependent on the web, and they also make use of various types of software applications nowadays. However, with time, such technologies are also evolving. Businesses are coping with huge amounts of data, and to… Read More »Five Major Benefits That Microsoft Power BI Brings To Data Scientists
The post Five Major Benefits That Microsoft Power BI Brings To Data Scientists appeared first on Data Science Central.
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https://github.com/minimaxir/imgbeddings
Instead, this package uses an ONNX INT8-quantized version of CLIP's Vision layers, which in testing works just as well, with a significant performance boost.
The demos also turned out very well, and try to a bit more fun than usual.
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Vision Transformer Cookbook
Hello, I have released the Vision Transformer Cookbook with Tensorflow !
Therefore, you can easy to use the 22 transformer architectures via just copy & paste.
I hope this repository would help many people, including tensorflow users.
Thank you.
* code: vit-tensorflow
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It is common for growing organizations to reach a point where their existing data solution is no longer adequate for their needs. In most cases, it happens with companies that have used an on-premises infrastructure from the earliest days of business but now need to upgrade their network for continued growth. However, relocating equipment and… Read More »Datacenter relocation is now easier, faster, and more affordable
The post Datacenter relocation is now easier, faster, and more affordable appeared first on Data Science Central.
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“Privid” could help officials gather secure public health data or enable transportation departments to monitor the density and flow of pedestrians, without learning personal information about people.
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One of the best known examples of GPT-3 for developers is the Github co-pilot Trained on billions of lines of public code, GitHub Copilot is more than autocomplete of code. GitHub Copilot is powered by Codex, the new AI system created by OpenAI. GitHub Copilot understands significantly more context than most code assistants. GitHub Copilot… Read More »GitHub Co-Pilot Alternatives: Can They Match the Functionality of Co-Pilot?
The post GitHub Co-Pilot Alternatives: Can They Match the Functionality of Co-Pilot? appeared first on Data Science Central.
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Despite the technological breakthroughs in the advent of Industry 4.0, manufacturers seem to have taken a more gradual approach to adoption. In 2020, less than 30 percent of the industry considered themselves extensive users of advanced integrated tools and processes. The pandemic, however, brought out an unprecedented need to explore opportunities that make manufacturing systems… Read More »Smart Maintenance – How SaaS Frameworks Turn Insights Into Actions Quickly And Efficiently
The post Smart Maintenance – How SaaS Frameworks Turn Insights Into Actions Quickly And Efficiently appeared first on Data Science Central.
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What is the toll-free number? Businesses provide a cloud-based contact number to allow customers to contact them free of cost. In India, this number- the business toll-free number is available in the 1800 series in an easily recognizable format- 1800-ABC-DEFG. Customers do not have to incur any fee to contact the business, as the company… Read More »Toll-free number: What is it, and how can you get one for your business?
The post Toll-free number: What is it, and how can you get one for your business? appeared first on Data Science Central.
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Found a useful list of Tools and Programs for AI/ML. Looks like it covers Machine Learning, Deep Learning, Computer Vision(CV), and Natural Language Processing (NLP). I thought I'd share it for anyone that's interested. https://github.com/mikeroyal/Machine-Learning-Guide
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I thought it will be quite interesting to see Deep Convolutional GAN’s capability in generating wildlife, so I wrote a tutorial on how to build a model based on the DCGAN architecture through PyTorch:
https://taying-cheng.medium.com/create-new-animals-using-dcgan-with-pytorch-2ce47810ebd4
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I recently wrote a critical review on "Deep Learning for Tabular Data" which reviews whether we are ready to move from Tree-based models to Neural Network-based models for Tabular data. It covers many novel approaches such as DeepInsight, IGTD and SuperTML. It also includes some of the transformers based recent works such as TabNet, Tab-Transformer, AutoInt, FT-Transformer and regularisation models such MLP+.
I have most commonly found the lack of a defined benchmark which makes it hard for people to find the right algorithms for the task. I am creating this discussion so that people who are using some of these algorithms or have tested some of them in different scenarios can share their findings.
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This post is co-written by Shibangi Saha, Data Scientist, and Graciela Kravtzov, Co-Founder and CTO, of Equilibrium Point. Many individuals are experiencing new symptoms of mental illness, such as stress, anxiety, depression, substance use, and post-traumatic stress disorder (PTSD). According to Kaiser Family Foundation, about half of adults (47%) nationwide have reported negative mental health […]
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Amazon Kendra is an intelligent search service powered by machine learning. You can receive spelling suggestions for misspelled terms in your queries by utilizing the Amazon Kendra Spell Checker. Spell Checker helps reduce the frequency of queries returning irrelevant results by providing spelling suggestions for unrecognized terms. In this post, we explore how to use […]
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When the first instant photo was taken 75 years ago with a Polaroid camera, it was groundbreaking to rapidly capture the 3D world in a realistic 2D image. Today, AI researchers are working on the opposite: turning a collection of still images into a digital 3D scene in a matter of seconds. Known as inverse Read article >
The post NVIDIA Research Turns 2D Photos Into 3D Scenes in the Blink of an AI appeared first on NVIDIA Blog.
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GeForce NOW gives you the power to game almost anywhere, at GeForce quality. And with the latest controller from SteelSeries, members can stay in control of the action on Android and Chromebook devices. This GFN Thursday takes a look at the SteelSeries Stratus+, now part of the GeForce NOW Recommended program. And it wouldn’t be Read article >
The post Take Control This GFN Thursday With New Stratus+ Controller From SteelSeries appeared first on NVIDIA Blog.
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The hum of a bustling data center is music to an AI developer’s ears — and NVIDIA data centers have found a rhythm of their own, grooving to the swing classic “Sing, Sing, Sing” in this week’s GTC keynote address. The lighthearted video, created with the NVIDIA Omniverse platform, features Louis Prima’s iconic music track, Read article >
The post Orchestrated to Perfection: NVIDIA Data Center Grooves to Tune of Millionfold Speedups appeared first on NVIDIA Blog.
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Introduction Splunk is a well-known log management tool. Splunk mines log from different machines in real-time and can be used to monitor, search, and analyze gathered data. It is a Big Data log management tool that can give insight from the unstructured data stored in the Splunk indexes. Splunk analytics helps turn unstructured log data… Read More »Business Analytics from Application Logs and Database using Splunk
The post Business Analytics from Application Logs and Database using Splunk appeared first on Data Science Central.
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I was reading this post about the TRPO algorithm. But I couldn't understand how we use MM algorithm in TRPO. I also watched some videos, they talked something about maximizing lower bound but I am not able to catch up what they are explaining. Can anyone explain this to me?
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The previous post covered the problem of oversiloing. Systems thinking, I pointed out, can help reduce the practice of siloing when it’s not necessary. In earlier posts, I’ve contrasted the difference between provincial IT and data-centric IT: Provincial IT is no longer necessary given the advances in compute, networking and storage we’ve seen over the… Read More »The long game: Feedback loops and desiloed systems by design (Part II of II)
The post The long game: Feedback loops and desiloed systems by design (Part II of II) appeared first on Data Science Central.
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When OpenAI released the third generation of their machine learning (ML) model that specializes in text generation in July 2020, I knew something was different. This model struck a nerve like no one that came before it. Suddenly I heard friends and colleagues, who might be interested in technology but usually don’t care much about […]
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This is a guest post co-authored by Taylor Names, Staff Machine Learning Engineer, Dev Gupta, Machine Learning Manager, and Argie Angeleas, Senior Product Manager at Ibotta. Ibotta is an American technology company that enables users with its desktop and mobile apps to earn cash back on in-store, mobile app, and online purchases with receipt submission, […]
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Autonomous vehicle development and validation require the ability to replicate real-world scenarios in simulation. At GTC, NVIDIA founder and CEO Jensen Huang showcased new AI-based tools for NVIDIA DRIVE Sim that accurately reconstruct and modify actual driving scenarios. These tools are enabled by breakthroughs from NVIDIA Research that leverage technologies such as NVIDIA Omniverse platform Read article >
The post NVIDIA Showcases Novel AI Tools in DRIVE Sim to Advance Autonomous Vehicle Development appeared first on NVIDIA Blog.
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This week at GTC, we’re celebrating – celebrating the amazing and impactful work that developers and startups are doing around the world. Nowhere is that more apparent than among the members of our global NVIDIA Inception program, designed to nurture cutting-edge startups who are revolutionizing industries. The program is free for startups of all sizes Read article >
The post NVIDIA Inception Introduces New and Updated Benefits for Startup Members to Accelerate Computing appeared first on NVIDIA Blog.
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The use of AI to write creative stories is increasing in popularity.
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Introduction/ Problem Splunk is a well-known log management tool. Splunk mines log from different machines in real-time and can be used to monitor, search, and analyze gathered data. It is a Big Data log management tool that can give insight from the unstructured data stored in the Splunk indexes. Splunk analytics helps turn unstructured log… Read More »Business Analytics from Application Logs and SQL Server Database using Splunk
The post Business Analytics from Application Logs and SQL Server Database using Splunk appeared first on Data Science Central.
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In many cases, for an enterprise to build its digital business technology platform, it must modernize its traditional data and analytics architecture. A modern data and analytics platform should be built on services-based principles and architecture. Introduction part 1, provided a conceptual-level reference architecture of a traditional Data and Analytics (D&A) platform. This part, provides… Read More »How to Modernize Enterprise Data and Analytics Platform (Part 2 of 4)
The post How to Modernize Enterprise Data and Analytics Platform (Part 2 of 4) appeared first on Data Science Central.
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AI has been making its way into the marketing world over the last few years. Businesses have touted it as the solution to their problems and a way to incorporate technology into their processes. But, how is AI changing SEO? How can you use AI to improve your business? Machine learning and artificial intelligence are… Read More »AI SEO: How AI Helps You Optimize Content for Search Results
The post AI SEO: How AI Helps You Optimize Content for Search Results appeared first on Data Science Central.
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Every time we think we have grasped a new technology and its use. Sometimes that shift is an increase in the technology itself that seemingly intensifies the original version. Sometimes something happens that causes a significant transformation in the technology’s nature. As the technology’s significance is increasingly understood, the name is altered to better reflect… Read More »Understanding the Role of Augmented Data Catalogs in Data Governance
The post Understanding the Role of Augmented Data Catalogs in Data Governance appeared first on Data Science Central.
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This post was co-written by John Heater, SVP of the Contact Center Practice at NeuraFlash. NeuraFlash is an Advanced AWS Partner with over 40 collective years of experience in the voice and automation space. With a dedicated team of conversation designers, data engineers, and AWS developers, NeuraFlash helps customers take advantage of the power of Amazon […]
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Amazon Search’s vision is to enable customers to search effortlessly. Our spelling correction helps you find what you want even if you don’t know the exact spelling of the intended words. In the past, we used classical machine learning (ML) algorithms with manual feature engineering for spelling correction. To make the next generational leap in […]
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https://arxiv.org/abs/2203.10977
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We're happy to announce the release of STUMPY v1.11.0! This version includes the oft requested Minkowski (p-norm) Distance, support for Multi-dimensional Motif Discovery, new Annotation vector tutorials, and enhancements for Pan Matrix Profiles!
https://github.com/TDAmeritrade/stumpy
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FDG2022: Foundations of Digital Games 2022Athens, Greece, September 5-8, 2022Conference website: http://fdg2022.org/
Foundations of Digital Games (FDG) 2022 invites research contributions in the form of papers, posters and demos, doctoral consortium applications, as well as panel, competition, and workshop proposals.
We invite contributions from within and across any discipline committed to advancing knowledge on the foundations of games: computer science and engineering, humanities and social sciences, arts and design, mathematics and natural sciences. As was the case in the previous years, we aim to publish the FDG 2022 proceedings in the ACM Digital Library. FDG invites authors to submit short or full papers reporting new research. Both short and full papers need to be anonymized and…
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Hi!
I have some experience deploying batch machine learning models and now I want to learn about real-time models. More specifically, how to put them in production and what are the best practices and tools for different use-cases.
Any ideas? I was thinking of reading the book "Designing Event-Driven Systems" by Ben Stopford (I think it's based on Kafka which seems quite popular), but would like to hear your thoughts or if someone has any other reference.
Thanks and I hope this is the right sub!
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Generative Adversarial Networks (GANs), with their capacity of producing high-quality images, have been the leading technology in image generation for the past couple of years. Nevertheless, their minimax learning mechanism brought out different limits, such as training instability and mode collapse (i.e., when all the produced samples belong to a small set of samples).
Recently, Generative Transformer models are beginning to match, or even surpass, the performances of GANs. The simple idea is to learn a function to encode the input image into a quantized sequence and then train an autoregressive Transformer on a sequence prediction task (i.e., predict an image token, given all the previous image tokens). This learning is based on maximum likelihood and thus not affected by the same issue…
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At GTC, NVIDIA announced significant updates for millions of creators using the NVIDIA Omniverse real-time 3D design collaboration platform. The announcements kicked off with updates to the Omniverse apps Create, Machinima and Showroom, with an immement View release. Powered by GeForce RTX and NVIDIA RTX GPUs, they dramatically accelerate 3D creative workflows. New Omniverse Connections Read article >
The post NVIDIA Omniverse Upgrade Delivers Extraordinary Benefits to 3D Content Creators appeared first on NVIDIA Blog.
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Digital artists and creative professionals have plenty to be excited about at NVIDIA GTC. Impressive NVIDIA Studio laptop offerings from ASUS and MSI launch with upgraded RTX GPUs, providing more options for professional content creators to elevate and expand creative possibilities. NVIDIA Omniverse gets a significant upgrade — including updates to the Omniverse Create, Machinima Read article >
The post At GTC: NVIDIA RTX Professional Laptop GPUs Debut, New NVIDIA Studio Laptops, a Massive Omniverse Upgrade and NVIDIA Canvas Update appeared first on NVIDIA Blog.
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Promising to transform trillion-dollar industries and address the “grand challenges” of our time, NVIDIA founder and CEO Jensen Huang Tuesday shared a vision of an era where intelligence is created on an industrial scale and woven into real and virtual worlds. Kicking off NVIDIA’s GTC conference, Huang introduced new silicon — including the new Hopper Read article >
The post Keynote Wrap Up: Turning Data Centers into ‘AI Factories,’ NVIDIA CEO Intros Hopper Architecture, H100 GPU, New Supercomputers, Software appeared first on NVIDIA Blog.
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The University of Florida’s academic health center, UF Health, has teamed up with NVIDIA to develop a neural network that generates synthetic clinical data — a powerful resource that researchers can use to train other AI models in healthcare. Trained on a decade of data representing more than 2 million patients, SynGatorTron is a language Read article >
The post Unlimited Data, Unlimited Possibilities: UF Health and NVIDIA Build World’s Largest Clinical Language Generator appeared first on NVIDIA Blog.
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Remote work and hybrid workplaces are the new normal for professionals in many industries. Teams spread throughout the world are expected to create and collaborate while maintaining top productivity and performance. Businesses use the NVIDIA RTX platform to enable their workers to keep up with the most demanding workloads, from anywhere. And today, NVIDIA is Read article >
The post New NVIDIA RTX GPUs Tackle Demanding Professional Workflows and Hybrid Work, Enabling Creation From Anywhere appeared first on NVIDIA Blog.
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Four NVIDIA Inception members have been selected as the first cohort of startups to access Cambridge-1, the U.K.’s most powerful supercomputer. The system will help British companies Alchemab Therapeutics, InstaDeep, Peptone and Relation Therapeutics enable breakthroughs in digital biology. Officially launched in July, Cambridge-1 — an NVIDIA DGX SuperPOD cluster powered by NVIDIA DGX A100 Read article >
The post First Wave of Startups Harnesses UK’s Most Powerful Supercomputer to Power Digital Biology Breakthroughs appeared first on NVIDIA Blog.
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When it comes to creating and connecting virtual worlds, over 150,000 individuals have downloaded NVIDIA Omniverse to make huge leaps in transforming 3D design workflows and achieve new heights of real-time, physically accurate simulations. At GTC, NVIDIA today announced new releases and updates for Omniverse — including the latest Omniverse Connectors and libraries — expanding Read article >
The post NVIDIA Omniverse Ecosystem Expands 10x, Amid New Features and Services for Developers, Enterprises and Creators appeared first on NVIDIA Blog.
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Next time socks, cereal or sandpaper shows up in hours delivered to your doorstep, consider the behind-the-scenes logistics acrobatics that help get them there so fast. Order fulfillment is a massive industry of moving parts. Heavily supported by autonomous mobile robots (AMRs), warehouses can span 1 million square feet, expanding and reconfiguring to meet demands. Read article >
The post NVIDIA Unveils Isaac Nova Orin to Accelerate Development of Autonomous Mobile Robots appeared first on NVIDIA Blog.
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Lucid Group may be a newcomer to the electric vehicle market, but its entrance has been grand. The electric automaker announced at GTC that its current and future fleets are built on NVIDIA DRIVE Hyperion for programmable, intelligent capabilities. By developing on the scalable, software-defined platform, Lucid ensures its vehicles are always at the cutting Read article >
The post Driving on Air: Lucid Group Builds Intelligent EVs on NVIDIA DRIVE appeared first on NVIDIA Blog.
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NVIDIA DRIVE Hyperion and DRIVE Orin are gaining ground in the industry. At NVIDIA GTC, BYD, the world’s second-largest electric vehicle maker, announced it is building its next-generation fleets on the DRIVE Hyperion architecture. This platform, based on DRIVE Orin, is now in production, and powering a wide ecosystem of 25 EV makers building software-defined Read article >
The post NVIDIA DRIVE Continues Industry Momentum With $11 Billion Pipeline as DRIVE Orin Enters Production appeared first on NVIDIA Blog.
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With a detailed knowledge of the world and everything in it, maps provide the foresight AI uses to make advanced and safe driving decisions. At his GTC keynote, NVIDIA founder and CEO Jensen Huang introduced NVIDIA DRIVE Map, a multimodal mapping platform designed to enable the highest levels of autonomy while improving safety. It combines Read article >
The post Announcing NVIDIA DRIVE Map: Scalable, Multi-Modal Mapping Engine Accelerates Deployment of Level 3 and Level 4 Autonomy appeared first on NVIDIA Blog.
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NVIDIA DRIVE Hyperion is taking software-defined vehicle architectures to the next level. At his GTC keynote, NVIDIA founder and CEO Jensen Huang announced DRIVE Hyperion 9, the next generation of the open platform for automated and autonomous vehicles. The programmable architecture, slated for 2026 production vehicles, is built on multiple DRIVE Atlan computers to achieve Read article >
The post Introducing NVIDIA DRIVE Hyperion 9: Next-Generation Platform for Software-Defined Autonomous Vehicle Fleets appeared first on NVIDIA Blog.
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Siemens Gamesa Renewable Energy is working with NVIDIA to create physics-informed digital twins of wind farms — groups of wind turbines used to produce electricity. The company has thousands of turbines around the globe that light up schools, homes, hospitals and factories with clean energy. In total they generate over 100 gigawatts of wind power, Read article >
The post Siemens Gamesa Taps NVIDIA Digital Twin Platform for Scientific Computing to Accelerate Clean Energy Transition appeared first on NVIDIA Blog.
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AT&T’s wireless network connects more than 100 million subscribers from the Aleutian Islands to the Florida Keys, spawning a big data sea. Abhay Dabholkar runs a research group that acts like a lighthouse on the lookout for the best tools to navigate it. “It’s fun, we get to play with new tools that can make Read article >
The post Speed Dialer: How AT&T Rings Up New Opportunities With Data Science appeared first on NVIDIA Blog.
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The NVIDIA Hopper GPU architecture unveiled today at GTC will accelerate dynamic programming — a problem-solving technique used in algorithms for genomics, quantum computing, route optimization and more — by up to 40x with new DPX instructions. An instruction set built into NVIDIA H100 GPUs, DPX will help developers write code to achieve speedups on Read article >
The post NVIDIA Hopper GPU Architecture Accelerates Dynamic Programming Up to 40x Using New DPX Instructions appeared first on NVIDIA Blog.
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The largest AI models can require months to train on today’s computing platforms. That’s too slow for businesses. AI, high performance computing and data analytics are growing in complexity with some models, like large language ones, reaching trillions of parameters. The NVIDIA Hopper architecture is built from the ground up to accelerate these next-generation AI Read article >
The post H100 Transformer Engine Supercharges AI Training, Delivering Up to 6x Higher Performance Without Losing Accuracy appeared first on NVIDIA Blog.
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Everyone wants to be heard. And with more people than ever in video calls or live streaming from their home offices, rich audio free from echo hiccups and background noises like barking dogs is key to better sounding online experiences. NVIDIA Maxine offers GPU-accelerated, AI-enabled software development kits to help developers build scalable, low-latency audio Read article >
The post NVIDIA Maxine Reinvents Real-Time Communication With AI appeared first on NVIDIA Blog.
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Using the Artificial Neural Network (ANN) to make a churn model, we will create a model that predicts a handwritten digit. (with source…
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Researchers from LinkedIn open-source the FastTreeSHAP package which is a Python module based on the paper ‘Fast TreeSHAP: Accelerating SHAP Value Computation for Trees.’ Implementing the widely-used TreeSHAP algorithm in the SHAP package allows for the efficient interpretation of tree-based machine learning models by estimating sample-level feature significance values. Its package includes two new algorithms: FastTreeSHAP v1 and FastTreeSHAP v2, both of which improve TreeSHAP’s computational efficiency by taking a different approach.
The empirical benchmarking tests show that FastTreeSHAP v1 is 1.5x faster than TreeSHAP while keeping memory costs the same, and FastTreeSHAP v2 is 2.5x faster while using slightly more memory. The FastTreeSHAP package fully supports parallel multi-core computing to speed up its computation.
Continue Reading The Full Summary Article
Paper: https://arxiv.org/pdf/2109.09847.pdf
Github: https://github.com/linkedin/fasttreeshap
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I asked this question on stats stackexchange, and it is posted here. I don't copy it here because formulas don't show up nicely on reddit.
I am trying to implement this paper by Fallah et al (NIPs 2021) titled: On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning. As the title suggests, they propose an algorithm for meta RL that uses an stochastic approximation of the gradient. My problem is with the term that yields the probability of a given trajectory (a sequence of state-actions). I don't know how to estimate that term and the paper doesn't discuss that. I'd appreciate if anyone can share any insight on how to estimate that term.
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I have made an animated video (https://www.youtube.com/watch?v=Rl-sFaE1z4M) for our ICLR 2022 paper (https://arxiv.org/abs/2203.09630).
Check it out if you are interested. I have made the video using 3b1b's manim library (https://github.com/ManimCommunity/manim).
Feedback is always very welcome!
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LinkedIn open sources the FastTreeSHAP Python package for efficient interpretation of tree-based ML models (XGBoost, LightGBM, sklearn random forest) using SHAPLEY. FastTreeSHAP v2 would be 2.5x faster than TreeSHAP. Let's reminder that SHAP (SHapley Additive exPlanation) values quantify the contribution of each feature to the model prediction, a bit like how each player contributes to the success of a sports team. SHAP does it by incorporating concepts from game theory and local explanations. Naively implemented, SHAP takes exponential time. LinkedIn blog post, scientific paper, and GitHub repo with IPython Notebooks.
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An author interview on the Equivariant Subgraph Aggregation Networks paper. Discusses why the expressive power of GNNs is limited and a method for breaking the bottleneck of the 1-WL algorithm
https://youtu.be/VYZog7kbXks
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Data visualization is an important aspect of all AI and machine learning applications. You can gain key insights of your […]
The post Data Visualization in Python with matplotlib, Seaborn and Bokeh appeared first on Machine Learning Mastery.
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The field of Artificial Intelligence (AI) continues to expand and improve by leaps and bounds. Today’s AI applications are becoming smarter…
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Figure 1: Airmass measurements over Ukraine from February 18, 2022 - March 01, 2022 from the SEVIRI instrument. Data accessed via the EUMETSAT Viewer.
Satellite imagery is a critical source of information during the current invasion of Ukraine. Military strategists, journalists, and researchers use this imagery to make decisions, unveil violations of international agreements, and inform the public of the stark realities of war. With Ukraine experiencing a large amount of cloud cover and attacks often occuring during night-time, many forms of satellite imagery are hindered from seeing the ground. Synthetic aperture radar imagery penetrates cloud cover, but requires special training to interpret. Automating this tedious task would enable real-time insights, but current computer vision meth…
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Does RepL4NLP accept papers such as pruning and quantization? The link below gives you a list of topics if you scroll down. One of them was " Efficient learning of representations and inference: with respect to training and inference time, model size, amount of training data, etc.". I was wondering if that has anything to do with pruning and/or quantization?
https://sites.google.com/view/repl4nlp2022/
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Seamless. Frictionless. Elegant. Efficient.
Read More
The post Building An Effective Experimentation Program – 01 Introduction appeared first on ML in Production.
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Seamless. Frictionless. Elegant. Efficient.
Read More
The post Building An Effective Experimentation Program – 01 Introduction appeared first on ML in Production.
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Hello everyone!
I’ve recently read Swin Transformer paper and tried to implement with PyTorch. But there’re no post that FULLY explains the nitty-gritty details of the paper with full implementation. It took me soooo long time to write this post so I wanted to share with y’all! Hope this helps someone! The implementation is based on the official implementation of Microsoft team.
https://jasonlee-cp.github.io/paper/Swin_Transformer/#swin-transformer-architecture
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You can find the paper here:
https://arxiv.org/abs/2201.11870
And the code and the data here:
https://github.com/p-karisani/CEPC
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Look for more info at: https://stackoverflow.com/questions/71533736/neural-network-is-training-to-give-just-one-output-how-can-i-prevent-this
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A project supported by the STFC Hartree Centre Discovery Accelerator accurately predicts patient response to treatments for ulcerative colitis and Crohn’s disease.
Artificial intelligence may soon assist more than 6 million1 individuals worldwide who suffer from inflammatory bowel disease (IBD) in selecting the optimum medication for their illness. An explainable AI pharmacogenomics methodology we created effectively predicted how patients will respond — favorably or negatively — to an IBD treatment 95% of the time, according to research published in PLOSone.
Chronic inflammatory bowel diseases (IBDs) such as ulcerative colitis and Crohn’s disease are caused by clinical, genetic, and environmental variables such as nutrition and lifestyle. Even though all patients have the same symptoms, there is no one-size-fits-all treatment for IBD that is helpful for everybody. Choosing the optimum therapy for a patient is still a trial-and-error procedure for both the doctor and the patient.
According to researchers at IBM Research in the UK and REPROCELL, a stem cell and fresh tissue research firm, used IBD patient data and explainable AI approaches to study treatment reactions with the help of the STFC Hartree Centre’s Discovery Accelerator. Their objective was to discover the optimum medications for IBD therapies less of a guessing game. The resulting collection of algorithms demonstrated that it was feasible to crack the IBD data black box and comprehend forecast and explain how persons with IBD could react to different medications on the market and under development.
Continue Reading Our Research Summary
Paper: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0263248
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Hello, we are publishing our first paper as undergraduate students today. It is achieving SOTA on the BDD100K dataset (2 out of 3 tasks, at least).
Paper: https://arxiv.org/abs/2203.09035
Code: https://github.com/datvuthanh/HybridNets
Network architecture:
HybridNets architecture
Contributions:
HybridNets, an end-to-end perception network, achieving outstanding results in real-time on the BDD100K dataset for 3 tasks: traffic object detection, drivable area segmentation (not SOTA), and lane line detection.
Automatically customized anchor for each level in the weighted bidirectional feature network, on any dataset.
An efficient training loss function and training strategy to balance and optimize multi-task networks.
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Conversational AI can deliver powerful, automated, interactive experiences through voice and text. Amazon Lex is a service that combines automatic speech recognition and natural language understanding technologies, so you can build these sophisticated conversational experiences. A common application of conversational AI is found in contact centers: self-service virtual agents. We’re excited to announce that you […]
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AI Weirdness: the strange side of machine learning
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NVIDIA’s GTC conference is packed with smart people and programming. The virtual gathering — which takes place from March 21-24 — sits at the intersection of some of the fastest-moving technologies of our time. It features a lineup of speakers from every corner of industry, academia and research who are ready to paint a high-definition Read article >
The post Hopped Up: NVIDIA CEO, AI Leaders to Discuss Next Wave of AI at GTC appeared first on NVIDIA Blog.
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Deepspeed? FSDP? FFCV? XYZK? what do they all mean and how can you use all of them to speed up your model training? All amazing techniques developed by world-class teams and are (or are being made) accessible via PyTorch Lightning!
If you know of other techniques you want to be integrated, please comment below!
https://william-falcon.medium.com/pytorch-lightning-vs-deepspeed-vs-fsdp-vs-ffcv-vs-e0d6b2a95719
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Hello,
Text processing AI has made great progress these last years but the main focus is on the English language (understandably). I think that many people are trying to do Natural Language Processing in non-English languages but are disappointed by the results. It is especially hard with text generation models like GPT-3, GPT-J, GPT-NeoX...
In this article, I'm trying to quickly summarize what the options are today for people trying to use a multilingual AI:
https://nlpcloud.io/multilingual-nlp-how-to-perform-nlp-in-non-english-languages.html
If you can think of additional solutions not mentioned in this article please let me know!
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A promising family of generative models has emerged: score-based generative models (SGMs) and denoising diffusion probabilistic models. SGMs have applications in image, voice, and music synthesis, image editing, super-resolution, image-to-image translation, and 3D shape generation because they provide high-quality synthesis and sample variety without requiring adversarial aims.
SGMs use a diffusion process to progressively introduce noise to the data, changing a complicated data distribution into a tractable prior distribution for analysis. The modified data’s score function—the gradient of the log probability density—is then learned using a neural network. To synthesize new samples, the learned scores can be used to solve a stochastic differential equation (SDE). Inverting the forward diffusion corresponds to an iterative denoising process.
Continue Reading
Paper: https://arxiv.org/pdf/2112.07068.pdf
Project: https://nv-tlabs.github.io/CLD-SGM/
Code: https://github.com/nv-tlabs/CLD-SGM
https://i.redd.it/8dl9ftuquzn81.gif
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Organizational forms serve as a primary business tool across industries—from financial services, to healthcare, and more. Consider, for example, tax filing forms in the tax management industry, where new forms come out each year with largely the same information. AWS customers across sectors need to process and store information in forms as part of their […]
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CSAIL scientists came up with a learning pipeline for the four-legged robot that learns to run entirely by trial and error in simulation.
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The AI-Guided Ultrasound Intervention Device is a lifesaving technology that helps a range of users deliver complex medical interventions at the point of injury.
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These are my favorite free Datacamp courses to learn in-demand data skills like Python, SQL, Power BI, Tableau, Seaborn, Matplotlib, Data…
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https://venturebeat.com/2021/11/15/astera-labs-announces-memory-acceleration-to-clear-datacenter-ai-ml-bottlenecks/
Do you think this technology would allow the use of clusters to handle larger data sets, thus reducing the overall cost of doing ML?
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https://medium.com/p/306fa7b7a80b
I believe a common misconception is that you only need to apply MLOps principles and tools if you are running hundreds of models. I'd argue it's not less important in a lot earlier stages of the model lifecycle.
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The [BigScience project](https://bigscience.huggingface.co) has just started the training of its main model and the training can be followed live here: https://twitter.com/BigScienceLLM and here: https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss
Here are more information on the model, dataset, engineering, training and hardware:
The model:
176B parameters decoder-only architecture (GPT-like)
70 layers - 112 attention heads per layers - hidden dimensionality of 14336 - 2048 tokens sequence length
ALiBi positional embeddings - GeLU activation function
Read more:
Blog post summarizing how the architecture, size, shape, and pre-training duration where selected: https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-…
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In this video, I discuss ORQA which uses a retriever to find the right context from the entire Wikipedia and then uses an extractive QA model to give a final answer. We discuss the task setup, architecture, and loss function.
The video is part of 8 video series on Open domain question answering, how it is different from normal QA, the difference in loss formulations, and key papers on different Open-QA architectures.
I will really appreciate any feedback.
https://www.youtube.com/watch?v=9bL2VbwZ9G8
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Hi MachineLearning,
I would like to introduce a new concept of utilizing factorized optical flow maps as mid-level representations, for bridging the perception and the control modules in modular learning based robotic frameworks.
In the below video, we demonstrate the DRL agent is able to control itself by perceiving the factorized optical flow maps, and without bumping into the pedestrians in the urban environment based on Unity.
Hope you like the idea and enjoy the video!
The screenshot from the demo video
Demo video: https://youtu.be/Op4QRTJOGMY
More details here: https://arxiv.org/abs/2203.04927
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deepmind/mctx: Monte Carlo tree search in JAX (github.com)
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In December 2020, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across popular ML tasks, as well as a selection of end-to-end solutions that […]
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MLK Visiting Professor S. Craig Watkins looks beyond algorithm bias to an AI future where models more effectively deal with systemic inequality.
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For context, I am a cofounder of Encord, a company building software to improve training data for computer vision.
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Hey everyone!
We just posted Part 2 of our Tutorial on Conformal Prediction and Distribution-Free Uncertainty Quantification on YouTube!
https://youtu.be/TRx4a2u-j7M
It focuses on conditional coverage and diagnostics to make sure your conformal procedure is working properly. It's slightly more advanced than the last one, but will leave you with a strong understanding of how to implement/evaluate conformal in code.
Let us know if you have any feedback by shooting me an email :)
Best,
Anastasios
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Wednesday night (3/16), 8-11 pm EDT, FSU professor and computer scientist Dr. Chris Mills will be the guest on Ask_a_Scientist_Gaming.
Chris’ research focus started in applications of machine learning to common software development tasks like concept location and traceability link recovery but has since broadened to applications of machine learning across many industries including finance and law. Current projects include building database-agnostic, natural language interfaces for question-and-answer systems with impedance reduction built from off-the-shelf object-relational mapping. With such an interface, users can directly answer questions and query data with no knowledge of a query language and no need to have custom reports constructed for each information need. Think “Jarvis,” but employees play the role of Iron Man at a bank… and a law firm…. and a hospital… and a university…. and the list goes on.
If you can’t make the live stream, feel free to leave your question in the comments and we will get them answered. Then follow up with our YouTube channel where we will post the video.
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Organizations use messaging platforms like Slack to bring the right people together to securely communicate with each other and collaborate to get work done. A Slack workspace captures invaluable organizational knowledge in the form of the information that flows through it as the users collaborate. However, making this knowledge easily and securely available to users […]
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Critical information can be scattered across multiple data sources in your organization, including sources such as Windows file systems stored on Amazon FSx for Windows File Server. You can now use the Amazon Kendra connector for FSx for Windows File Server to index documents (HTML, PDF, MS Word, MS PowerPoint, and plain text) stored in […]
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In this post, we demonstrate how to create an automated email response solution using Amazon Comprehend. Organizations spend lots of resources, effort, and money on running their customer care operations to answer customer questions and provide solutions. Your customers may ask questions via various channels, such as email, chat, or phone, and deploying a workforce […]
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We’ve released new versions of GPT-3 and Codex which can edit or insert content into existing text, rather than just completing existing text. These new capabilities make it practical to use the OpenAI API to revise existing content, such as rewriting a paragraph of text or refactoring code.
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Calculus for Machine Learning Crash Course. Get familiar with the calculus techniques in machine learning in 7 days. Calculus is […]
The post Calculus for Machine Learning (7-day mini-course) appeared first on Machine Learning Mastery.
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I blew it last week. I’ll readily admit it. Blame it on the flu or Covid or whatever the nasty bug was that confined me to bed for a day and fuzzy for a few. It’s not often that the 15th of March happens to come up on the same day as the weekly newsletter… Read More »DSC Weekly Digest 15 March 2022: Beware the Ides of …
The post DSC Weekly Digest 15 March 2022: Beware the Ides of … appeared first on Data Science Central.
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What is Data Fabric ?
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A machine-learning model for image classification that’s trained using synthetic data can rival one trained on the real thing, a study shows.
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Made a super tiny library that hashes your data and compares the hashes to determine if you have samples leaked into the other dataset.
Main usage is to add one line of code before your training loop as an extra check.
Useage is as easy as: python spills = check_spill(train_loader, test_loader)
Github: https://github.com/LaihoE/did-it-spill Currently only for PyTorch
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Hi folks,
SuperAnnotate is launching webinar series on automated computer vision pipelines, and the first episode is here for you to check out!
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This is a post co-written with Bernard Paques, CTO of Storm Reply, and Karl Herkt, Senior Strategist at Dassault Systèmes 3DExcite. While computer vision can be crucial to industrial maintenance, manufacturing, logistics, and consumer applications, its adoption is limited by the manual creation of training datasets. The creation of labeled pictures in an industrial context […]
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Are you aware of the technicalities involved in making Machine Learning models holistic, intuitive, and impactful? If not, you first need…
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A considerable part of the population uses mobile devices and computers to search data. They also store and perform data procedures. However, not everyone is aware of the relevance of data backup. Your crucial data is essential for anything that you do. It is here that technical support companies can help. Whether it’s your desktop… Read More »Why is Data Back-Up Necessary? The Benefits of Availing Technical Support
The post Why is Data Back-Up Necessary? The Benefits of Availing Technical Support appeared first on Data Science Central.
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In the past, Metadata Management is used to know how to use data catalog to find simple data or a book or a periodical in a library. However, today it is one of the most critical data practices for a successful organization dealing with data. With the rise of distributed architectures, including cloud & big… Read More »Why do you need a metadata management system? Definition and Benefits.
The post Why do you need a metadata management system? Definition and Benefits. appeared first on Data Science Central.
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Just wanted to ask anyone with experience working in the field of implicit neural represenations regarding the compute requirements you've experienced when developing models. Mainly looking in the domain of neural radiance fields (https://www.matthewtancik.com/nerf). I do have cluster access for evaluating projects that are more mature in the development pipeline, but wanted to gauge if anyone had any advice regarding what has worked when still in earlier development mainly when working on my standalone PC.
Thanks so much for any help!
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A well popularized article in Quanta magazine ask the question « Will Transformers Take Over Artificial Intelligence? ». Since having revolutionized NLP, attention is conquering computer vision and reinforcement learning. I find pretty unfortunate that the attention mechanism was totally eclipsed by Transformers which is just a funny name (animation movie/ toy) for self-attention architecture, although the Google's paper title on Transformers was «Attention is all you need».
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Ice protects the Earth layer and its oceans by acting as a shield. Excess heat is reflected into space by these dazzling white spots, keeping the Earth cold. Many glaciers throughout the world have been melting quickly since the early 1900s. Human actions cause this phenomenon. Carbon dioxide (CO2) and other greenhouse gas emissions have elevated temperatures since the industrial revolution.
Melting glaciers are a contributing factor in rising sea levels, which leads to an increase of coastal erosion and storm surge. Warmer air temperatures lead directly into more frequent storms like hurricanes or typhoons with stronger winds that cause even greater damage on land. Many cities are already planning to deal with long-term flooding, which may carry salt and moisture into houses and infrastructure, jeopardize drinking water and agriculture, and severely damaged ports.
Given the gravity of the problem, it is critical to understand how much and how quickly sea levels will rise. The projections in the existing predictive models made by scientists are pretty uncertain. Since the contribution from the southernmost continent is so unknown, governments worldwide must consider an unlimited number of scenarios when planning for the future.
A group of Stanford University scientists employed autonomous drone technology and machine learning approach to focus their efforts on discovering and gathering the most valuable data in Antarctica to increase our understanding of the processes that drive sea-level rise.
Continue Reading Our Summary on This Research From Stanford or checkout the HAI Report
submitted by /u/No_Coffee_4638
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Hey My Reddit Fellows,
I just wanted to share a video series I am making about AGI, how to manage AGI Safety, and what the post singularity society will look like. Please subscribe to my channel, and let me know if you have any feedback and what topics you would like to see next!
►Playlist: https://youtube.com/playlist?list=PLb4nW1gtGNse4PA_T4FlgzU0otEfpB1q1
►AGI Existential Threat: https://youtu.be/V4iQP7VDMvI
►Life 3.0: https://youtu.be/aWlSwZKzmzY
►Dangers of AGI Sub Goals: https://youtu.be/_-tQH03rq4g
►How to Create an AGI: https://youtu.be/7OHhqli9oaA
Thank you!
Bill
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I recently had a long conversation with Tim Scarfe and Keith Duggar on Machine Learning Street Talk (MLST) about theory related to neural networks. I really believe we can make better machine learning algorithms and better guarantees if we uncover the right theoretical track. I would really appreciate hearing from you all in the community about this work, so I've written up this post to accompany the MLST video. Enjoy!
See the full interactive version of this post on my research page here.
Get the code for these experiments here.
Data Distributions and Initializing Neural Networks
Is it possible for us to make fixed-size multilayer perceptrons (MLP's) provably converge? It's been bothering me that initialization seems arbitrary and all the optimization algorithms produce different resu…
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https://nn.labml.ai/transformers/retro/model.html
This is an annotated (side-by-side notes) implementation of RETRO in PyTorch.
Retrieval Enhanced Transformer (RETRO) is 25X smaller than GPT-3 but has comparable performance. It uses chunks of similar text retrieved based on a frozen BERT model from a massive database (5 trillion tokens) to improve the performance of the model. Since the model can retrieve information from this large database it doesn't have to contain all the facts in the model weights.
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The news was announced here.
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Hi folks. I'm a new DL researcher who just began working at a startup that focuses on AI-based drug discovery. I'm afraid this is not the most suitable place to post this since this is more of an engineering idea, but I wanted to hear what you guys think about it and if you have any idea.
I don't know how many of you have encountered the same efficiency issue before, but I've repeatedly come across this theme while implementing my research ideas:
I have a dataset that consists of datapoints with non-uniform length along some dimensions (number of atoms in a molecule, number of amino acids in a protein etc.), and I want to perform numerical calculations (e.g. feed into a DL model) on a tensorized-batched form of them. The batching (turning them into a single tensor) would be an indispensi…
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Scientists conduct trial and error procedures which experimenting, that many times lear to freat scientific breakthroughs. Similarly, foundational research provides for developing large-scale AI systems theoretical insights that reduce the amount of trial and error required and can be very cost-effective.
Microsoft team tunes massive neural networks that are too expensive to train several times. For this, they employed a specific parameterization that maintains appropriate hyperparameters across varied model sizes. The used µ-Parametrization (or µP, pronounced “myu-P”) is a unique way to learn all features in the infinite-width limit. The researchers collaborated with the OpenAI team to test the method’s practical benefit on various realistic cases.
Studies have shown that training large neural networks because their behavior changes as they grow in size are uncertain. Many works suggest heuristics that attempt to maintain consistency in the activation scales at initialization. However, as training progresses, this uniformity breaks off at various model widths.
CONTINUE READING MY SUMMARY ON THIS RESEARCH
Paper: https://www.microsoft.com/en-us/research/uploads/prod/2021/11/TP5.pdf
Github:https://github.com/microsoft/mup
https://i.redd.it/wu93hpd7wvm81.gif
submitted by /u/No_Coffee_4638
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Dears.
I have more than 5 years experience in machine learning and deep learning and recently have created a Youtube channel. https://www.youtube.com/channel/UCn9Rujwh7SfHF2RRvy_ks-g In the channel, I first explain a paper, then I implement/explain the code .
Please join, leave a comment, and share with your friends. You can also suggest any paper and I will add it to my list.
I am constantly trying to improve contents and quality.
Thanks.
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https://pytorch.org/blog/pytorch-1.11-released/
As a longtime TensorFlow user I've been meaning to switch to either JAX or PyTorch, thus I'm pretty intrigued by this.
In the past I've been having a hard time giving up tf.data's pretty elegant fluent interface for performant I/O and data preprocessing. Has anyone tried the new PyTorch equivalent? How does TorchData stack up?
And are there more things in JAX that functorch cannot express or will both autograd engines hit feature parity now-ish?
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Check to understand how image recognition technology works and why image detection revolutionizes business.
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Chemical engineers use neural networks to discover the properties of metal-organic frameworks, for catalysis and other applications.
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We’ve recently launched our machine learning profiler https://github.com/graphsignal/graphsignal to make ML profiling simple and usable. It automatically provides operation and kernel level statistics as well as detailed resource usage information necessary for making training and inference faster and more efficient.
More details and screenshots in the blog post https://graphsignal.com/blog/machine-learning-profiler-for-training-and-inference/.
I hope some of you find it useful. Any feedback is appreciated.
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For the 14th consecutive year, each Academy Award nominee for the Best Visual Effects used NVIDIA technologies. The 94th annual Academy Awards ceremony, taking place Sunday, March 27, has five nominees in the running: Dune Free Guy No Time to Die Shang-Chi and the Legend of the Ten Rings Spider-Man: No Way Home NVIDIA has Read article >
The post At the Movies: For 14th Year Running, NVIDIA Technologies Power All VFX Oscar Nominees appeared first on NVIDIA Blog.
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Say hello to tomorrow’s smart electric meter, literally. You can ask some next-generation home energy hubs questions, just like you do Alexa or Siri. Some devices, arriving this year, will display real-time simulations — vibrant as a video game — to show how you can lower your energy bill or reduce your carbon footprint. They’ll Read article >
The post Light Me Up: Innovators Redefine Energy Meters for a More Efficient Grid appeared first on NVIDIA Blog.
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The GeForce NOW RTX 3080 membership gives gamers unrivaled performance from the cloud – with latency so low that it feels just like playing on a local PC. Today, gamers can experience RTX 3080-class streaming at only $19.99 a month, thanks to GeForce NOW’s new monthly membership plans*. It’s a great chance to experience powerful Read article >
The post GeForce NOW RTX 3080 One-Month Memberships Now Available appeared first on NVIDIA Blog.
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In this post, we will demonstrate how to securely launch notebook instances in a private subnet of an Amazon Virtual Private Cloud (Amazon VPC), with internet access disabled, and to securely connect to Amazon Simple Storage Service (Amazon S3) using VPC endpoints. This post is for network and security architects that support decentralized data science […]
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Many software as a service (SaaS) providers across various industries are adding machine learning (ML) and artificial intelligence (AI) capabilities to their SaaS offerings to address use cases like personalized product recommendation, fraud detection, and accurate demand protection. Some SaaS providers want to build such ML and AI capabilities themselves and deploy them in a […]
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Amazon SageMaker Autopilot is an automated machine learning (AutoML) solution that performs all the tasks you need to complete an end-to-end machine learning (ML) workflow. It explores and prepares your data, applies different algorithms to generate a model, and transparently provides model insights and explainability reports to help you interpret the results. Autopilot can also […]
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In this post, we present a solution for digitizing transactional documents using Amazon Textract and incorporate a human review using Amazon Augmented AI (A2I). You can find the solution source at our GitHub repository. Organizations must frequently process scanned transactional documents with structured text so they can perform operations such as fraud detection or financial […]
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AWESOME MACHINE LEARNING
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